论文标题
使用智能手机识别人类活动的分类
Classification of human activity recognition using smartphones
论文作者
论文摘要
智能手机是通信方式中最受欢迎和使用最广泛的设备。如今，通过嵌入式传感器可以在移动设备上进行人类活动识别，可以通过预测用户活动来利用这些传感器来管理移动设备上的用户行为。为了达到这一目标，研究了这项研究的活动特征，分类并将其映射到学习算法中。在这项研究中，我们通过深信仰网络应用分类来测试和训练数据，这导致了98.25％的培训数据诊断和93.01％的测试数据。因此，在这项研究中，我们证明了深信网络是针对此特定目的的合适方法。
Smartphones have been the most popular and widely used devices among means of communication. Nowadays, human activity recognition is possible on mobile devices by embedded sensors, which can be exploited to manage user behavior on mobile devices by predicting user activity. To reach this aim, storing activity characteristics, Classification, and mapping them to a learning algorithm was studied in this research. In this study, we applied categorization through deep belief network to test and training data, which resulted in 98.25% correct diagnosis in training data and 93.01% in test data. Therefore, in this study, we prove that the deep belief network is a suitable method for this particular purpose.
